An ensemble model of self-organizing maps for imputation of missing values

Abstract

The purpose of this study is to improve the accuracy of missing value estimation by using self-organizing maps (SOMs). We propose an ensemble model of self-organizing maps, a new method for the imputation of missing values, which is an important preprocessing step in data analysis. Learning results of self-organizing maps have diversity because the self-organizing map's learning algorithm has a dependence on initial values; this property can be used to contribute to improving the accuracy of ensemble learning. In this study, we estimated missing values by an ensemble learning procedure that leverages the initial value dependence of the SOM. We tested the effectiveness of the proposed method by computational experiments using data published in the UCI Machine Learning Repository. Our experimental results confirmed that the proposed method produced higher accuracy than a conventional SOM when estimating values that were randomly set to missing.